macula densa subtype analysis
jeff karnsomprot
2025-04-18
1 Macula Densa Project
1.1 Objectives
Three Main Goals of this File
Produce Cleaner looking code.
Identify the amount of clusters there are
Identify the top genes
expressed in each of the clusters
2 Loading in Data sets + Library packages.
##
## The downloaded binary packages are in
## /var/folders/7n/x74qctp91rng390gx0z9hmd80000gn/T//RtmpIXQzec/downloaded_packagesoptions(future.globals.maxSize = 74 * 1024^3) # 55 GB
getOption("future.globals.maxSize") #59055800320## [1] 794568949763 Analyzing DATASET
SO4@meta.data$sample <- factor(
SO4@meta.data$sample,
levels = c("SO1","SO4","SO2","SO3"))
DimPlot(SO4,split.by ="sample",group.by = "sample")
3.1 Viewing for different DEGs
markers.to.plot1 <- c(
"Atf3", #
"Egr1", #
"Fos", #
"Jun", #
"Junb", #
"Pappa2", #
"Cxcl10", #
"Cldn19", #
"Krt7", #
"Egf", #
"Ptger3",
"Ckb",
"Mcub",
"Fabp3",
"Foxq1",
"Vash2",
"Pamr1",
"Vegfa"
)
DotPlot(SO4,
features = markers.to.plot1,
dot.scale = 8,
dot.min = 0,
scale = FALSE,
scale.max = 100,
scale.min = 0,
col.min = -2.5,
group.by = "seurat_clusters",
col.max = 2.5)+
coord_flip()
##
## Attaching package: 'plotly'## The following object is masked from 'package:AnnotationDbi':
##
## select## The following object is masked from 'package:IRanges':
##
## slice## The following object is masked from 'package:S4Vectors':
##
## rename## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layout3.2 Finding Markers for each MD Subtype
Idents(SO4) <- "subclass2_MD"
MD_DEGs <- FindAllMarkers(SO4,
only.pos = TRUE,
logfc.threshold = 0.1,
min.pct = 0.1,
min.diff.pct = 0.1,
return.thresh = 0.05)## Calculating cluster type_1## Warning: The `slot` argument of `GetAssayData()` is deprecated as of SeuratObject 5.0.0.
## ℹ Please use the `layer` argument instead.
## ℹ The deprecated feature was likely used in the Seurat package.
## Please report the issue at <https://github.com/satijalab/seurat/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: `PackageCheck()` was deprecated in SeuratObject 5.0.0.
## ℹ Please use `rlang::check_installed()` instead.
## ℹ The deprecated feature was likely used in the Seurat package.
## Please report the issue at <https://github.com/satijalab/seurat/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## For a (much!) faster implementation of the Wilcoxon Rank Sum Test,
## (default method for FindMarkers) please install the presto package
## --------------------------------------------
## install.packages('devtools')
## devtools::install_github('immunogenomics/presto')
## --------------------------------------------
## After installation of presto, Seurat will automatically use the more
## efficient implementation (no further action necessary).
## This message will be shown once per session## Calculating cluster type_2## Calculating cluster type_3## Calculating cluster type_4MD_DEGs <- MD_DEGs %>% arrange(desc(avg_log2FC))
write.xlsx(MD_DEGs, file = here("jk_code", "DEGs_MD_SUBclass.xlsx"), rowNames = TRUE)
# save as excel file
MD_DEGs %>%
group_by(cluster) %>%
dplyr::filter(avg_log2FC > 1, p_val_adj < 0.05) %>%
slice_head(n = 10) %>%
ungroup() -> top10
# save as excel file
DoHeatmap(SO4, features = top10$gene) + NoLegend()## Warning in DoHeatmap(SO4, features = top10$gene): The following features were
## omitted as they were not found in the scale.data slot for the SCT assay: Oasl2
3.3 Markers for each MD Sub-SUb type
Idents(SO4) <- "subclass_MD"
MD_DEGs2 <- FindAllMarkers(SO4,
only.pos = TRUE,
logfc.threshold = 0.1,
min.pct = 0.1,
min.diff.pct = 0.1,
return.thresh = 0.05)## Calculating cluster type_1## Calculating cluster type_2a## Calculating cluster type_2b## Calculating cluster type_3## Calculating cluster type_4MD_DEGs2 <- MD_DEGs2 %>% arrange(desc(avg_log2FC))
write.xlsx(MD_DEGs2, file = here("jk_code", "DEGs_MD_SUBSUBclass.xlsx"), rowNames = TRUE)
# save as excel file
MD_DEGs2 %>%
group_by(cluster) %>%
dplyr::filter(avg_log2FC > 1, p_val_adj < 0.05) %>%
slice_head(n = 10) %>%
ungroup() -> top10
# save as excel file
DoHeatmap(SO4, features = top10$gene) + NoLegend()## Warning in DoHeatmap(SO4, features = top10$gene): The following features were
## omitted as they were not found in the scale.data slot for the SCT assay: Oasl2,
## Atp6v0d2, Irx3
3.4 Finding markers between each type now
3.4.1 Type 1a Markers
## Warning: The `slot` argument of `FetchData()` is deprecated as of SeuratObject 5.0.0.
## ℹ Please use the `layer` argument instead.
## ℹ The deprecated feature was likely used in the Seurat package.
## Please report the issue at <https://github.com/satijalab/seurat/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
